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  • 學位論文

地工階段施工損鄰費用預估之研究—以大台北地區為例

A Study on Estimating the Cost of Adjacent Property Damage Caused by Foundation Excavation-Take Taipei Metropolitan Area for Example

指導教授 : 陳柏翰
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摘要


大台北地區地窄人稠的都市空間,房屋建築緊鄰而立的景象四處可見,加上都市型態快速發展,新舊建築物混雜其中,而且建築工程中地質情況無法百分之百的完全預測,建築物龜裂、倒塌的情況常有所聞,故損鄰事件對營建施工上有一定風險之存在。 營造廠商於施工損鄰事件中常處於弱勢之ㄧ方,面對民眾與公部門(建管單位)之責難與索賠,多半選擇「賠錢了事」之消極心態。有鑑於此,參與建案投標之營造廠商需事前做好現況調查及工法評估,詳列可能遭逢之損鄰肇事因子,並提出因應對策及評估可能之損鄰費用,面對接踵而來的損鄰賠償支出,才不致陷入營建成本超支之困境。 本研究蒐集國內相關文獻並參酌大台北地區建案之損鄰賠償資料,經由分析及歸納整理出地工階段損鄰發生之肇事因子,進行預試將上述資料加以編修,建立出預估施工損鄰費用之14個評選主項,內含了52個評選子項。接著運用MATLAB軟體建立類神經網路模型,並利用各專案之相關損鄰資料來訓練該模型,經過持續不斷的反饋與修正可得出最適宜之網路模型。日後於專案投標階段針對地工損鄰費用之預估,即可利用填選該專案符合之評選因子,經由上述之類神經網路模型來試算出損鄰金額與建築工程總價之佔比。 建議其他營造廠商若欲使用本研究之預估模型,可依照自身之經驗及賠償案例,自行調整評選之架構與配分,再利用本研究所建構之類神經網路模型試算出地工階段損鄰預估金額與建築工程總價之佔比。

並列摘要


Taipei metropolitan area is a densely populated urban place. Side-by-side crowded building can be seen everywhere. Not only does rapid urban development result that old and new buildings are mixed together, but also buildings are crack or fallen down because the geological condition could not be fully predicted. Therefore, the certain risk exists on the adjacent property damage caused by construction. Construction companies are always on the disadvantaged side for the disputed events. To face the blame and claim from the public or the government, they mostly choose payout to avoid the consequence trouble. Based on this situation, construction companies need to do investigations and engineering methods assessment in advance. They should analyze the accident factors, provide the strategy and estimate the relative expense to face the claim of disputed events in order not to over the construction budget. This study collected relevant domestic literature and deliberated the Taipei metropolitan data about the adjacent property damage caused by construction. By analyzing the accidents and doing a pilot test, I figured out 14 primary factors that contain 52 secondary items. Using MATLAB software created a neural network model for designing the optimal model. Thereafter, I chose the relevant factors and used above neural network model to estimate the cost of adjacent property damage caused by foundation excavation on the bidding stage. In order to estimate the cost, each company should consider the former experiences and compensation cases of itself to adjust the factors of model.

參考文獻


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